#include <ocr_det.h>


namespace PaddleOCR {

    void DBDetector::LoadModel(const std::string& model_dir) {
        //   AnalysisConfig config;
        paddle_infer::Config config;
        config.SetModel(model_dir + "/inference.pdmodel",
            model_dir + "/inference.pdiparams");

        if (this->use_gpu_) {
            config.EnableUseGpu(this->gpu_mem_, this->gpu_id_);
            if (this->use_tensorrt_) {
                auto precision = paddle_infer::Config::Precision::kFloat32;
                if (this->precision_ == "fp16") {
                    precision = paddle_infer::Config::Precision::kHalf;
                }
                if (this->precision_ == "int8") {
                    precision = paddle_infer::Config::Precision::kInt8;
                }
                config.EnableTensorRtEngine(
                    1 << 20, 10, 3,
                    precision,
                    false, false);
                std::map<std::string, std::vector<int>> min_input_shape = {
                    {"x", {1, 3, 50, 50}},
                    {"conv2d_92.tmp_0", {1, 96, 20, 20}},
                    {"conv2d_91.tmp_0", {1, 96, 10, 10}},
                    {"nearest_interp_v2_1.tmp_0", {1, 96, 10, 10}},
                    {"nearest_interp_v2_2.tmp_0", {1, 96, 20, 20}},
                    {"nearest_interp_v2_3.tmp_0", {1, 24, 20, 20}},
                    {"nearest_interp_v2_4.tmp_0", {1, 24, 20, 20}},
                    {"nearest_interp_v2_5.tmp_0", {1, 24, 20, 20}},
                    {"elementwise_add_7", {1, 56, 2, 2}},
                    {"nearest_interp_v2_0.tmp_0", {1, 96, 2, 2}} };
                std::map<std::string, std::vector<int>> max_input_shape = {
                    {"x", {1, 3, this->max_side_len_, this->max_side_len_}},
                    {"conv2d_92.tmp_0", {1, 96, 400, 400}},
                    {"conv2d_91.tmp_0", {1, 96, 200, 200}},
                    {"nearest_interp_v2_1.tmp_0", {1, 96, 200, 200}},
                    {"nearest_interp_v2_2.tmp_0", {1, 96, 400, 400}},
                    {"nearest_interp_v2_3.tmp_0", {1, 24, 400, 400}},
                    {"nearest_interp_v2_4.tmp_0", {1, 24, 400, 400}},
                    {"nearest_interp_v2_5.tmp_0", {1, 24, 400, 400}},
                    {"elementwise_add_7", {1, 56, 400, 400}},
                    {"nearest_interp_v2_0.tmp_0", {1, 96, 400, 400}} };
                std::map<std::string, std::vector<int>> opt_input_shape = {
                    {"x", {1, 3, 640, 640}},
                    {"conv2d_92.tmp_0", {1, 96, 160, 160}},
                    {"conv2d_91.tmp_0", {1, 96, 80, 80}},
                    {"nearest_interp_v2_1.tmp_0", {1, 96, 80, 80}},
                    {"nearest_interp_v2_2.tmp_0", {1, 96, 160, 160}},
                    {"nearest_interp_v2_3.tmp_0", {1, 24, 160, 160}},
                    {"nearest_interp_v2_4.tmp_0", {1, 24, 160, 160}},
                    {"nearest_interp_v2_5.tmp_0", {1, 24, 160, 160}},
                    {"elementwise_add_7", {1, 56, 40, 40}},
                    {"nearest_interp_v2_0.tmp_0", {1, 96, 40, 40}} };

                config.SetTRTDynamicShapeInfo(min_input_shape, max_input_shape,
                    opt_input_shape);
            }
        }
        else {
            config.DisableGpu();
            if (this->use_mkldnn_) {
                config.EnableMKLDNN();
                // cache 10 different shapes for mkldnn to avoid memory leak
                config.SetMkldnnCacheCapacity(10);
            }
            config.SetCpuMathLibraryNumThreads(this->cpu_math_library_num_threads_);
        }
        // use zero_copy_run as default
        config.SwitchUseFeedFetchOps(false);
        // true for multiple input
        config.SwitchSpecifyInputNames(true);

        config.SwitchIrOptim(true);

        config.EnableMemoryOptim();
        // config.DisableGlogInfo();

        this->predictor_ = CreatePredictor(config);
    }

    void DBDetector::Run(cv::Mat& img,
        std::vector<std::vector<std::vector<int>>>& boxes,
        std::vector<double>* times) {
        float ratio_h{};
        float ratio_w{};

        cv::Mat srcimg;
        cv::Mat resize_img;
        img.copyTo(srcimg);

        auto preprocess_start = std::chrono::steady_clock::now();
        this->resize_op_.Run(img, resize_img, this->max_side_len_, ratio_h, ratio_w,
            this->use_tensorrt_);

        this->normalize_op_.Run(&resize_img, this->mean_, this->scale_,
            this->is_scale_);

        std::vector<float> input(1 * 3 * resize_img.rows * resize_img.cols, 0.0f);
        this->permute_op_.Run(&resize_img, input.data());
        auto preprocess_end = std::chrono::steady_clock::now();

        // Inference.
        auto input_names = this->predictor_->GetInputNames();
        auto input_t = this->predictor_->GetInputHandle(input_names[0]);
        input_t->Reshape({ 1, 3, resize_img.rows, resize_img.cols });
        auto inference_start = std::chrono::steady_clock::now();
        input_t->CopyFromCpu(input.data());

        this->predictor_->Run();

        std::vector<float> out_data;
        auto output_names = this->predictor_->GetOutputNames();
        auto output_t = this->predictor_->GetOutputHandle(output_names[0]);
        std::vector<int> output_shape = output_t->shape();
        int out_num = std::accumulate(output_shape.begin(), output_shape.end(), 1,
            std::multiplies<int>());

        out_data.resize(out_num);
        output_t->CopyToCpu(out_data.data());
        auto inference_end = std::chrono::steady_clock::now();

        auto postprocess_start = std::chrono::steady_clock::now();
        int n2 = output_shape[2];
        int n3 = output_shape[3];
        int n = n2 * n3;

        std::vector<float> pred(n, 0.0);
        std::vector<unsigned char> cbuf(n, ' ');

        for (int i = 0; i < n; i++) {
            pred[i] = float(out_data[i]);
            cbuf[i] = (unsigned char)((out_data[i]) * 255);
        }

        cv::Mat cbuf_map(n2, n3, CV_8UC1, (unsigned char*)cbuf.data());
        cv::Mat pred_map(n2, n3, CV_32F, (float*)pred.data());

        const double threshold = this->det_db_thresh_ * 255;
        const double maxvalue = 255;
        cv::Mat bit_map;
        cv::threshold(cbuf_map, bit_map, threshold, maxvalue, cv::THRESH_BINARY);
        cv::Mat dilation_map;
        cv::Mat dila_ele = cv::getStructuringElement(cv::MORPH_RECT, cv::Size(2, 2));
        cv::dilate(bit_map, dilation_map, dila_ele);
        boxes = post_processor_.BoxesFromBitmap(
            pred_map, dilation_map, this->det_db_box_thresh_,
            this->det_db_unclip_ratio_, this->use_polygon_score_);

        boxes = post_processor_.FilterTagDetRes(boxes, ratio_h, ratio_w, srcimg);
        auto postprocess_end = std::chrono::steady_clock::now();
        std::cout << "Detected boxes num: " << boxes.size() << std::endl;

        std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
        times->push_back(double(preprocess_diff.count() * 1000));
        std::chrono::duration<float> inference_diff = inference_end - inference_start;
        times->push_back(double(inference_diff.count() * 1000));
        std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
        times->push_back(double(postprocess_diff.count() * 1000));

        //// visualization
        if (this->visualize_) {
            Utility::VisualizeBboxes(srcimg, boxes);
        }
    }

} // namespace PaddleOCR
